The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification

Fiber-optic immunosensor based on a Fabry-Perot interferometer for single-molecule detection of biomarkers

Immunosensors capable of ultralow-concentration and single-molecule detection of biomarkers are garnering attention for the early diagnosis of cancer. Herein, a fiber-optic Fabry-Perot interferometer (FPI)-based immunosensor was used for the first time for single-molecule detection of progastrin-releasing peptide (ProGRP). The cascaded FPI structure of the immunosensor introduces a high-order harmonic Vernier effect (HVE). A piece of a side-polished D-shaped hollow-core photonic crystal fiber (HCPCF) was used as a sensing FPI, on which the biomarker was deposited to detect ProGRP. Compared with traditional FPIs with open-cavity structures, this structure provided a larger contact area and improved the sensitivity of the immunosensor. The polished side surface of the D-shaped HCPCF was modified using a gold nanoparticle-graphene oxide (AuNP@GO) nanointerface to enhance refractive index (RI) modulation via antigen-antibody binding and achieve selective energy enhancement of the binding site. The antigen binding changes the RI of the D-shaped HCPCF and the effective RI of the transmitted light in the sensing FPI, thereby changing the spectrum of the immunosensor. Experimental results showed that the high-order HVE and AuNP@GO nanointerface considerably improved the immunosensor sensitivity, exhibiting a liquid RI sensitivity of 583,000 nm/RIU. After functionalization with an anti-ProGRP antibody, the limit of detection of the immunosensor for ProGRP reached 17.1 ag/mL; moreover, the immunosensor could perform detection at the single-molecule level. The proposed novel immunosensor overcomes the sensitivity limitations of optical devices and achieves single-molecule detection of a protein.

Ancient forest plants possess cytotoxic properties causing liver cancer HepG2 cell apoptosis

Here, we collected 154 plant species in China ancient forests looking for novel efficient bioactive compounds for cancer treatments. We found 600 bioactive phyto-chemicals that induce apoptosis of liver cancer cell in vitro. First, we screen the plant extract's in vitro cytotoxicity inhibition of cancer cell growth using in vitro HepG2 cell lines and MTT cytotoxicity. The results from these initial MTT in vitro cytotoxicity tests show that the most efficient plants towards hepatoma cytoxicity is Cephalotaxus sinensis, mint bush (Elsholtzia stauntonii) and winged spindle tree (Euonymus alatus). We then used in cell-counting kit-8 (CCK-8) to further understand in vivo tumor growth using nude mice and GC-MS and LC-QTOF-MS to analyze the composition of compounds in the extracts. Extracted chemically active molecules analyzed by network pharmacology showed inhibition on the growth of liver cancer cells by acting on multiple gene targets, which is different from the currently used traditional drugs acting on only one target of liver cancer cells. Extracts from Cephalotaxus sinensis, mint bush (Elsholtzia stauntonii) and winged spindle tree (Euonymus alatus) induce apoptosis in hepatoma cancer cell line HepG2 with a killing rate of more than 83% and a tumor size decrease by 62-67% and a killing rate of only 6% of normal hepatocyte LO2. This study highlight efficient candidate species for cancer treatment providing a basis for future development of novel plant-based drugs to help meeting several of the UN SDGs and planetary health.

Proteomic analyses identify HK1 and ATP5A to be overexpressed in distant metastases of lung adenocarcinomas compared to matched primary tumors

Lung cancer is the leading cause of cancer-related deaths worldwide with lung adenocarcinoma (LUAD) being the most common type. Genomic studies of LUAD have advanced our understanding of its tumor biology and accelerated targeted therapy. However, the proteomic characteristics of LUAD are still insufficiently explored. The prognosis for lung cancer patients is still mostly determined by the stage of disease at the time of diagnosis. Focusing on late-stage metastatic LUAD with poor prognosis, we compared the proteomic profiles of primary tumors and matched distant metastases to identify relevant and potentially druggable differences. We performed high-performance liquid chromatography (HPLC) and electrospray ionization tandem mass spectrometry (ESI-MS/MS) on a total of 38 FFPE (formalin-fixed and paraffin-embedded) samples. Using differential expression analysis and unsupervised clustering we identified several proteins that were differentially regulated in metastases compared to matched primary tumors. Selected proteins (HK1, ATP5A, SRI and ARHGDIB) were subjected to validation by immunoblotting. Thereby, significant differential expression could be confirmed for HK1 and ATP5A, both upregulated in metastases compared to matched primary tumors. Our findings give a better understanding of tumor progression and metastatic spreads in LUAD but also demonstrate considerable inter-individual heterogeneity on the proteomic level.

Advances in mass spectrometry-based multi-scale metabolomic methodologies and their applications in biological and clinical investigations

Metabolomics is a nascent field of inquiry that emerged in the late 20th century. It encompasses the comprehensive profiling of metabolites across a spectrum of organisms, ranging from bacteria and cells to tissues. The rapid evolution of analytical methods and data analysis has greatly accelerated progress in this dynamic discipline over recent decades. Sophisticated techniques such as liquid chromatograph mass spectrometry (MS), gas chromatograph MS, capillary electrophoresis MS, and nuclear magnetic resonance serve as the cornerstone of metabolomic analysis. Building upon these methods, a plethora of modifications and combinations have emerged to propel the advancement of metabolomics. Despite this progress, scrutinizing metabolism at the single-cell or single-organelle level remains an arduous task over the decades. Some of the most thrilling advancements, such as single-cell and single-organelle metabolic profiling techniques, offer profound insights into the intricate mechanisms within cells and organelles. This allows for a comprehensive study of metabolic heterogeneity and its pivotal role in multiple biological processes. The progress made in MS imaging has enabled high-resolution in situ metabolic profiling of tissue sections and even individual cells. Spatial reconstruction techniques enable the direct representation of metabolic distribution and alteration in three-dimensional space. The application of novel metabolomic techniques has led to significant breakthroughs in biological and clinical studies, including the discovery of novel metabolic pathways, determination of cell fate in differentiation, anti-aging intervention through modulating metabolism, metabolomics-based clinicopathologic analysis, and surgical decision-making based on on-site intraoperative metabolic analysis. This review presents a comprehensive overview of both conventional and innovative metabolomic techniques, highlighting their applications in groundbreaking biological and clinical studies.

LC-MS/MS: A sensitive and selective analytical technique to detect COVID-19 protein biomarkers in the early disease stage

INTRODUCTION

The COVID-19 outbreak has put enormous pressure on the scientific community to detect infection rapidly, identify the status of disease severity, and provide an immediate vaccine/drug for the treatment. Relying on immunoassay and a real-time reverse transcription polymerase chain reaction (rRT-PCR) led to many false-negative and false-positive reports. Therefore, detecting biomarkers is an alternative and reliable approach for determining the infection, its severity, and disease progression. Recent advances in liquid chromatography and mass spectrometry (LC-MS/MS) enable the protein biomarkers even at low concentrations, thus facilitating clinicians to monitor the treatment in hospitals.

AREAS COVERED

This review highlights the role of LC-MS/MS in identifying protein biomarkers and discusses the clinically significant protein biomarkers such as Serum amyloid A, Interleukin-6, C-Reactive Protein, Lactate dehydrogenase, D-dimer, cardiac troponin, ferritin, Alanine transaminase, Aspartate transaminase, gelsolin and galectin-3-binding protein in COVID-19, and their analysis by LC-MS/MS in the early stage.

EXPERT OPINION

Clinical doctors monitor significant biomarkers to understand, stratify, and treat patients according to disease severity. Knowledge of clinically significant COVID-19 protein biomarkers is critical not only for COVID-19 caused by the coronavirus but also to prepare us for future pandemics of other diseases in detecting by LC-MS/MS at the early stages.

An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data

Analyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise. Additionally, DL-based workflows are often hindered to be integrated into medical settings due to the lack of interpretable explanation. We present DLearnMS, a DL biomarker detection framework, to address these challenges on proteomics instances of liquid chromatography-mass spectrometry (LC-MS) - a well-established tool for quantifying complex protein mixtures. Our DLearnMS framework learns the clinical state of LC-MS data instances using convolutional neural networks. Based on the trained neural networks, we show how biomarkers can be identified using layer-wise relevance propagation. This enables detecting discriminating regions of the data and the design of more robust networks. One of the main advantages over other established methods is that no explicit preprocessing step is needed in our DLearnMS framework. Our evaluation shows that DLearnMS outperforms conventional LC-MS biomarker detection approaches in identifying fewer false positive peaks while maintaining a comparable amount of true positives peaks. Code availability: The code is available from the following GIT repository: https://github.com/SaharIravani/DlearnMS.

Identification of potential plasma protein biomarkers for feline pancreatic carcinoma by liquid chromatography tandem mass spectrometry

In both humans and cats, pancreatic carcinoma is an aggressive cancer with a grave prognosis. Proteomics techniques have successfully identified several blood-based biomarkers of human pancreatic neoplasia. Thus, this study aims to investigate whether similar biomarkers can be identified in the plasma of cats with FePAC by using liquid chromatography tandem mass spectrometry (LC-MS/MS). To facilitate evaluation of the low abundance plasma proteome, a human-based immunodepletion device (MARS-2) was first validated for use with feline plasma. Marked reduction and/or complete removal of albumin and immunoglobulins was confirmed by analysis of electrophoretograms and mass spectral data. Subsequently, plasma collected from 9 cats with pancreatic carcinoma (FePAC), 10 cats with symptomatic pancreatitis, and 10 healthy control cats was immunodepleted and subjected to LC-MS/MS. Thirty-seven plasma proteins were found to be differentially expressed (p < .05 in one-way ANOVA, FC >2 in fold change analysis). Among these proteins, ETS variant transcription factor 4 (p < .05) was overexpressed, while gelsolin (p < .01), tryptophan 2,3-dioxygenase (p < .05), serpin family F member 1 (p < .01), apolipoprotein A-IV (p < .01) and phosphatidylinositol-glycan-specific phospholipase D (p < .05) were down-regulated in cats with FePAC. Further studies on these potential biomarkers are needed to investigate their diagnostic value.

High expression of protein tyrosine phosphatase receptor S (PTPRS) is an independent prognostic marker for cholangiocarcinoma

Cholangiocarcinoma (CCA) is an aggressive tumor of the bile duct with a high rate of mortality. Lymph node metastasis is an important factor facilitating the progression of CCA. A reliable biomarker for diagnosis, progression status, or prognosis of CCA is still lacking. To identify a novel and reliable biomarker for diagnosis/prognosis of CCA, liquid chromatography-mass spectrometry and tandem mass spectrometry (LC-MS/MS) in combination with bioinformatics analysis were applied for the representative serum samples of patients with CCA. The proteome results showed that protein tyrosine phosphatase receptor S (PTPRS) had the highest potential candidate. Then, a dot blot assay was used to measure the level of serum PTPRS in patients with CCA (n = 80), benign biliary disease patients (BBD; n = 39), and healthy controls (HC; n = 55). PTPRS level of CCA sera (14.38 ± 9.42 ng/ml) was significantly higher than that of BBD (10.7 ± 5.05 ng/ml) or HC (6 ± 3.73 ng/ml) (P < 0.0001). PTPRS was associated with serum albumin (P = 0.028), lymph node metastasis (P = 0.038), and the survival time of patients (P = 0.011). Using a log-rank test, higher serum PTPRS level was significantly (P = 0.031) correlated with a longer overall survival time of patients with CCA, and PTPRS was an independent prognostic marker for CCA superior to carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA) or alkaline phosphatase (ALP). High expression of PTPRS could be a good independent prognostic marker for CCA.

Batch alignment via retention orders for preprocessing large-scale multi-batch LC-MS experiments

MOTIVATION

Meticulous selection of chromatographic peak detection parameters and algorithms is a crucial step in preprocessing LC-MS data. However, as mass-to-charge ratio (m/z) and retention time shifts are larger between batches than within batches, finding apt parameters for all samples of a large-scale multi-batch experiment with the aim of minimizing information loss becomes a challenging task. Preprocessing independent batches individually can curtail said problems but requires a method for aligning and combining them for further downstream analysis.

RESULTS

We present two methods for aligning and combining individually preprocessed batches in multi-batch LC-MS experiments. Our developed methods were tested on six sets of simulated and six sets of real datasets. Furthermore, by estimating the probabilities of peak insertion, deletion, and swap between batches in authentic datasets we demonstrate that retention order swaps are not rare in untargeted LC-MS data.

AVAILABILITY

kmersAlignment and rtcorrectedAlignment algorithms are made available as an R package with raw data at https://metabocombiner.img.cas.cz.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Proteomic Profiling and Biomarker Discovery in Colorectal Liver Metastases

Colorectal liver metastases (CRLM) are the leading cause of death among patients with metastatic colorectal cancer (CRC). As part of multimodal therapy, liver resection is the mainstay of curative-intent treatment for select patients with CRLM. However, effective treatment of CRLM remains challenging as recurrence occurs in most patients after liver resection. Proposed clinicopathologic factors for predicting recurrence are inconsistent and lose prognostic significance over time. The rapid development of next-generation sequencing technologies and decreasing DNA sequencing costs have accelerated the genomic profiling of various cancers. The characterisation of genomic alterations in CRC has significantly improved our understanding of its carcinogenesis. However, the functional context at the protein level has not been established for most of this genomic information. Furthermore, genomic alterations do not always result in predicted changes in the corresponding proteins and cancer phenotype, while post-transcriptional and post-translational regulation may alter synthesised protein levels, affecting phenotypes. More recent advancements in mass spectrometry-based technology enable accurate protein quantitation and comprehensive proteomic profiling of cancers. Several studies have explored proteomic biomarkers for predicting CRLM after oncologic resection of primary CRC and recurrence after curative-intent resection of CRLM. The current review aims to rationalise the proteomic complexity of CRC and explore the potential applications of proteomic biomarkers in CRLM.

Mass Spectrometry-Based Proteomics for Biomarker Discovery

Proteomics plays a pivotal role in systems medicine, in which pharmacoproteomics and toxicoproteomics have been developed to address questions related to efficacy and toxicity of drugs. Mass spectrometry is the core technology for quantitative proteomics, providing the capabilities of identification and quantitation of thousands of proteins. The technology has been applied to biomarker discovery and understanding the mechanisms of drug action. Both stable isotope labeling of proteins or peptides and label-free approaches have been incorporated with multidimensional LC separation and tandem mass spectrometry (LC-MS/MS) to increase the coverage and depth of proteome analysis. A protocol of such an approach exemplified by dimethyl labeling in combination with 2D-LC-MS/MS is described. With further development of novel proteomic tools and increase in sample throughput, the full spectrum of mass spectrometry-based proteomic research will greatly advance systems medicine.

High-throughput Proteomic Profiling of Male Breast Cancer Tissue

BACKGROUND/AIM

Until now, little emphasis has been placed on the protein expression profile of male breast cancer (MBC) tumors, due to the rarity of the disease. The present study aimed to identify a proteomic pattern that is characteristic for malignant male breast tissue epithelium.

MATERIALS AND METHODS

The protein content of four male breast tumors and corresponding adjacent healthy (control) tissues was analyzed by high-throughput nano-liquid chromatography-MS/MS technology.

RESULTS

A total of 2,352 proteins were identified, that correspond to 1,249 single gene products, with diverse biological roles. Of those, a panel of 119 differentially expressed tissue proteins was identified in MBC samples compared to controls; 90 were found to be over-expressed in MBC tissues, while 29 were down-regulated. Concurrently, 844 proteins were detected only in MBC tumors and 197 were expressed exclusively in control mammary samples.

CONCLUSION

Differential proteomic expression was found in MBC tissue, leading to improved understanding of MBC pathology and highlighting the need for personalized management of male patients.

Aptamers and New Bioreceptors for the Electrochemical Detection of Biomarkers Expressed in Hepatocellular Carcinoma

Hepatocellular carcinoma is a malignancy associated with high mortality and increasing incidence. Early detection of this disease could help increase survival and overall patient benefit. Non-invasive strategies for the diagnosis of this medical condition are of utmost importance. In this scope, the detection of hepatocellular carcinoma biomarkers could provide a useful diagnostic tool. Aptamers represent as short, single-stranded DNAs or RNAs that can specifically bind selected analytes, and also as pseudo-biorecognition elements that can be employed for electrode functionalization. Also, other types of DNA sequences can be used for the construction of DNA-based biosensors applied for the quantification of hepatocellular carcinoma biomarkers. Herein, we will be analyzing recent examples of aptasensors and DNA biosensors for the detection of hepatocellular carcinoma biomarkers like micro-RNAs, long non-coding RNAs, exosomes, circulating tumor cells and proteins. The literature data is discussed comparatively in a critical manner highlighting the advantages of using electrochemical biosensors in diagnosis, as well as the use of nanomaterials and biocomponents in the functionalization of electrodes for improved sensitivity and selectivity.

ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.

Longitudinal stability of urinary extracellular vesicle protein patterns within and between individuals

The protein content of urinary extracellular vesicles (EVs) is considered to be an attractive non-invasive biomarker source. However, little is known about the consistency and variability of urinary EV proteins within and between individuals over a longer time-period. Here, we evaluated the stability of the urinary EV proteomes of 8 healthy individuals at 9 timepoints over 6 months using data-independent-acquisition mass spectrometry. The 1802 identified proteins had a high correlation amongst all samples, with 40% of the proteome detected in every sample and 90% detected in more than 1 individual at all timepoints. Unsupervised analysis of top 10% most variable proteins yielded person-specific profiles. The core EV-protein-interaction network of 516 proteins detected in all measured samples revealed sub-clusters involved in the biological processes of G-protein signaling, cytoskeletal transport, cellular energy metabolism and immunity. Furthermore, gender-specific expression patterns were detected in the urinary EV proteome. Our findings indicate that the urinary EV proteome is stable in longitudinal samples of healthy subjects over a prolonged time-period, further underscoring its potential for reliable non-invasive diagnostic/prognostic biomarkers.

Mass spectrometry in the lipid study of cancer

Introduction: Cancer is a heterogeneous disease that exploits various metabolic pathways to meet the demand for increased energy and structural components. Lipids are biomolecules that play essential roles as high energy sources, mediators, and structural components of biological membranes. Accumulating evidence has established that altered lipid metabolism is a hallmark of cancer.Areas covered: Mass spectrometry (MS) is a label-free analytical tool that can simultaneously identify and quantify hundreds of analytes. To date, comprehensive lipid studies exclusively rely on this technique. Here, we reviewed the use of MS in the study of lipids in various cancers and discuss its instrumental limitations and challenges.Expert opinion: MS and MS imaging have significantly contributed to revealing altered lipid metabolism in a variety of cancers. Currently, a single MS approach cannot profile the entire lipidome because of its lack of sensitivity and specificity for all lipid classes. For the metabolic pathway investigation, lipid study requires the integration of MS with other molecular approaches. Future developments regarding the high spatial resolution, mass resolution, and sensitivity of MS instruments are warranted.

Screening of important metabolites and KRAS genotypes in colon cancer using secondary ion mass spectrometry

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is an imaging-based analytical technique that can characterize the surfaces of biomaterials. We used TOF-SIMS to identify important metabolites and oncogenic KRAS mutation expressed in human colorectal cancer (CRC). We obtained 540 TOF-SIMS spectra from 180 tissue samples by scanning cryo-sections and selected discriminatory molecules using the support vector machine (SVM) algorithm. Each TOF-SIMS spectrum contained nearly 860,000 ion profiles and hundreds of spectra were analyzed; therefore, reducing the dimensionality of the original data was necessary. We performed principal component analysis after preprocessing the spectral data, and the principal components (20) of each spectrum were used as the inputs of the SVM algorithm using the R package. The performance of the algorithm was evaluated using the receiver operating characteristic (ROC) area under the curve (AUC) (0.9297). Spectral peaks (m/z) corresponding to discriminatory molecules used to classify normal and tumor samples were selected according to p-value and were assigned to arginine, α-tocopherol, and fragments of glycerophosphocholine. Pathway analysis using these discriminatory molecules showed that they were involved in gastrointestinal disease and organismal abnormalities. In addition, spectra were classified according to the expression of KRAS somatic mutation, with 0.9921 AUC. Taken together, TOF-SIMS efficiently and simultaneously screened metabolite biomarkers and performed KRAS genotyping. In addition, a machine learning algorithm was provided as a diagnostic tool applied to spectral data acquired from clinical samples prepared as frozen tissue slides, which are commonly used in a variety of biomedical tests.

Serum N-glycan profiles differ for various breast cancer subtypes

Breast cancer is the most prevalent cancer in women. Early detection of this disease improves survival and therefore population screenings, based on mammography, are performed. However, the sensitivity of this screening modality is not optimal and new screening methods, such as blood tests, are being explored. Most of the analyses that aim for early detection focus on proteins in the bloodstream. In this study, the biomarker potential of total serum N-glycosylation analysis was explored with regard to detection of breast cancer. In an age-matched case-control setup serum protein N-glycan profiles from 145 breast cancer patients were compared to those from 171 healthy individuals. N-glycans were enzymatically released, chemically derivatized to preserve linkage-specificity of sialic acids and characterized by high resolution mass spectrometry. Logistic regression analysis was used to evaluate associations of specific N-glycan structures as well as N-glycosylation traits with breast cancer. In a case-control comparison three associations were found, namely a lower level of a two triantennary glycans and a higher level of one tetraantennary glycan in cancer patients. Of note, various other N-glycomic signatures that had previously been reported were not replicated in the current cohort. It was further evaluated whether the lack of replication of breast cancer N-glycomic signatures could be partly explained by the heterogenous character of the disease since the studies performed so far were based on cohorts that included diverging subtypes in different numbers. It was found that serum N-glycan profiles differed for the various cancer subtypes that were analyzed in this study.

Genetic Mutation Analysis in Small Cell Lung Cancer by a Novel NGS-Based Targeted Resequencing Gene Panel and Relation with Clinical Features

Background

Small cell lung cancer (SCLC) is an aggressive and invasive malignancy that presents at advanced clinical stage with no more effective treatments. Development of a method for its early detection would be useful, also new therapeutic target need to be discovered; however, there is a lack of information about its oncogenic driver gene mutations.

Objectives

We aim to identify the SCLC-related genomic variants that associate with clinical staging and serum protein biomarkers observed in other types of lung cancer.

Methods

We screened formalin-fixed paraffin-embedded (FFPE) biopsy tissues of 32 Chinese SCLC patients using the 303 oncogenic driver gene panel generated by Tiling PCR amplification sequencing (tPAS) and analyzed the patients' corresponding serum protein levels of CYFRA21-1 CEA, NSE, and SCCA.

Results

In total, we found 147 SCLC-related mutant genes, among these, three important genes (TP53, RB1, KMT2D) as well as five novel genes LRRK2, BRCA1, PTCH1, ARID2, and APC that altogether occurred in 90% of patients. Furthermore, increased mutations to 6 genes (WT1, NOTCH1, EPHA3, KDM6A, SETD2, ACVR1B) significantly associated with higher serum NSE levels (P = 0.0016) and higher clinical stages II + III compared to stage I (P = 0.06).

Conclusions

Our panel is relatively reliable in detecting the oncogenic mutations of Chinese SCLC patients. Based on our findings, it may be possible to combine SCLC-related mutations and serum NSE for a simple detection of clinical staging.

Design and Characterization of a Novel Blood Collection and Transportation Device for Proteomic Applications

A major hurdle for blood-based proteomic diagnostics is efficient transport of specimens from the collection site to the testing laboratory. Dried blood spots have shown utility for diagnostic applications, specifically those where red blood cell hemolysis and contamination of specimens with hemoglobin is not confounding. Conversely, applications that are sensitive to the presence of the hemoglobin subunits require blood separation, which relies on centrifugation to collect plasma/serum, and then cold-chain custody during shipping. All these factors introduce complexities and potentially increased costs. Here we report on a novel whole blood-collection device (BCD) that efficiently separates the liquid from cellular components, minimizes hemolysis in the plasma fraction, and maintains protein integrity during ambient transport. The simplicity of the design makes the device ideal for field use. Whole blood is acquired through venipuncture and applied to the device with an exact volume pipette. The BCD design was based on lateral-flow principles in which whole blood was applied to a defined area, allowing two minutes for blood absorption into the separation membrane, then closed for shipment. The diagnostic utility of the device was further demonstrated with shipments from multiple sites (n = 33) across the U.S. sent to two different centralized laboratories for analyses using liquid chromatography/mass spectrometry (LC/MS/MS) and matrix assisted laser desorption/ionization-time of flight (MALDI-ToF) commercial assays. Specimens showed high levels of result label concordance for the LC/MS/MS assay (Negative Predictive Value = 98%) and MALDI-ToF assay (100% result concordance). The overall goal of the device is to simplify specimen transport to the laboratory and produce clinical test results equivalent to established collection methods.

Immunoaffinity microflow liquid chromatography/tandem mass spectrometry for the quantitation of PD1 and PD-L1 in human tumor tissues

RATIONALE

High tumor expression of programmed cell death protein (PD1) and programmed death-ligand 1 (PD-L1) is thought to be associated with positive clinical outcomes after treatment with anti-PD1 or anti-PD-L1 agents. Several sensitive methods based on immunohistochemistry, ligand binding assay (LBA), and liquid chromatography/mass spectrometry involving the measurement of PD1 and PD-L1 expression have been reported. Here, we expand on the characterization of different tumor types using a highly specific, sensitive, and robust immunoaffinity liquid chromatography/tandem mass spectrometry (IA-LC/MS/MS)-based method for the simultaneous quantitation of PD1 and PD-L1 in tumor tissues.

METHODS

Human tumor tissue samples were homogenized using a Precellys Evolution homogenizer. The samples were incubated with anti-PD1 and anti-PD-L1 capture polyclonal antibodies, which were bound to magnetic beads. Following enrichment, samples were digested with trypsin. A Waters iKEY HSS T3 1.8 um (150 μm × 100 mm) column with a gradient flow rate of 3 μL/min was used for chromatographic separation, and a Waters TQ-S triple quadrupole mass spectrometer was used for detection. Selected reaction monitoring (SRM) transitions with unit resolution for precursor/product ion masses were optimized for PD1 and PD-L1 surrogate peptides.

RESULTS

The surrogate peptides LAAFPEDR for PD1 and FTVTVPK for PD-L1 yielded the most intense SRM transitions at m/z 459.7 > 516.2 and m/z 396.2 > 543.3, respectively, and thus were selected for the quantitation of PD1 and PD-L1. The lower limit of quantitation for PD1 and PD-L1 was 0.062 ng/mL with an assay range up to 10 ng/mL. Using this method, human PD1 and PD-L1 were detected and quantified from four different types of tumor tissues. The data show that PD1 expression level was highly correlated with that of PD-L1 in all tumor tissues analyzed here.

CONCLUSIONS

A highly specific and sensitive immunoaffinity microflow LC/MS/MS method for the simultaneous quantification of PD1 and PD-L1 in tumor tissues was developed and implemented. This method combines the advantage of immuno-capture for analyte enrichment with the high specificity of detection of multiple surrogate peptides by LC/MS/MS. The quantification of PD1 and PD-L1 co-expression in tumor could help evaluate their role in assessing tumor type selection and patient stratification.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

Quantifying Serum Derived Differential Expressed and Low Molecular Weight Protein in Breast Cancer Patients

Background:

Searching the biomarker from complex heterogeneous material for early detection of disease is a challenging task in the field of biomedical sciences.

Objective:

The study has been arranged to explore the proteomics serum derived profiling of the differential expressed and low molecular weight protein in breast cancer patient.

Methods:

Quantitative proteome was analyzed using the Nano LC/Mass and Bioinformatics tool.

Results:

This quantification yields 239 total protein constituting 29% of differentially expressed protein, with 82% downregulated differential protein and 18% up-regulated differential protein. While 12% of total protein were found to be cancer inducing proteins. Gene Ontology (GO) described that the altered proteins with 0-60 kDa mass in nucleus, cytosol, ER, and mitochondria were abundant that chiefly controlled the RNA, DNA, ATP, Ca ion and receptor bindings.

Conclusion:

The study demonstrate that the organelle specific, low molecular weighted proteins are significantly important biomarker. That act as strong agents in the prognosis and diagnosis of breast cancer at early stage.

Proteomic analysis enables distinction of early- versus advanced-stage lung adenocarcinomas

Background

A gel‐free proteomic approach was utilized to perform in‐depth tissue protein profiling of lung adenocarcinoma (ADC) and normal lung tissues from early and advanced stages of the disease. The long‐term goal of this study is to generate a large‐scale, label‐free proteomics dataset from histologically well‐classified lung ADC that can be used to increase further our understanding of disease progression and aid in identifying novel biomarkers.

Methods and results

Cases of early‐stage (I‐II) and advanced‐stage (III‐IV) lung ADCs were selected and paired with normal lung tissues from 22 patients. The histologically and clinically stratified human primary lung ADCs were analyzed by liquid chromatography‐tandem mass spectrometry. From the analysis of ADC and normal specimens, 4863 protein groups were identified. To examine the protein expression profile of ADC, a peak area‐based quantitation method was used. In early‐ and advanced‐stage ADC, 365 and 366 proteins were differentially expressed, respectively, between normal and tumor tissues (adjusted P‐value < .01, fold change ≥ 4). A total of 155 proteins were dysregulated between early‐ and advanced‐stage ADCs and 18 were suggested as early‐specific stage ADC. In silico functional analysis of the upregulated proteins in both tumor groups revealed that most of the enriched pathways are involved in mRNA metabolism. Furthermore, the most overrepresented pathways in the proteins that were unique to ADC are related to mRNA metabolic processes.

Conclusions

Further analysis of these data may provide an insight into the molecular pathways involved in disease etiology and may lead to the identification of biomarker candidates and potential targets for therapy. Our study provides potential diagnostic biomarkers for lung ADC and novel stage‐specific drug targets for rational intervention.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

Development of a Robust Ultrasonic-Based Sample Treatment To Unravel the Proteome of OCT-Embedded Solid Tumor Biopsies

An effective three-step proteomics workflow is proposed to overcome the pitfalls caused by polymers present in optimum cutting temperature (OCT)-embedded tissue during its preparation for mass spectrometry analysis. First, the OCT-embedded tissue biopsies are cleaned using ethanol and water in a sequential series of ultrasonic washes in an ultrasound bath (35 kHz ultrasonic frequency, 100% ultrasonic amplitude, 2 min of ultrasonic duty time). Second, a fast ultrasonic-assisted extraction of proteins is done using an ultrasonic probe (30 kHz ultrasonic frequency, 50% ultrasonic amplitude, 2 min of ultrasonic duty time, 1 mm diameter tip). Third, a rapid ultrasonic digestion of complex proteomes is performed using a microplate horn assembly device (20 kHz ultrasonic frequency, 25% ultrasonic amplitude, 4 min of ultrasonic duty time). As a proof of concept, the new workflow was applied to human normal and tumor kidney biopsies including chromophobe renal cell carcinomas (chRCCs) and renal oncocytomas (ROs). A successful cluster of proteomics profiles was obtained comprising 511 and 172 unique proteins found in chRCC and RO samples, respectively. The new method provides high sample throughput and comprehensive protein recovery from OCT samples.

Applying Machine Learning of Erythrocytes Dynamic Antigens Store in Medicine

Erythrocytes Dynamic Antigens Store (EDAS) is a new discovery. EDAS consists of self-antigens and foreign (non-self) antigens. In patients with infectious diseases or malignancies, antigens of infection microorganism or malignant tumor exist in EDAS. Storing EDAS of normal individuals and patients in a database has, at least, two benefits. First, EDAS can be mined to determine biomarkers representing diseases which can enable researchers to develop a new line of laboratory diagnostic tests and vaccines. Second, EDAS can be queried, directly, to reach a precise diagnosis without the need to do many laboratory tests. The target is to find the minimum set of proteins that can be used as biomarkers for a particular disease. A hypothetical EDAS is created. Hundred-thousand records are randomly generated. The mathematical model of hypothetical EDAS together with the proposed techniques for biomarker discovery and direct diagnosis are described. The different possibilities that may occur in reality are experimented. Biomarkers' proteins are identified for pathogens and malignancies, which can be used to diagnose conditions that are difficult to diagnose. The presented tool can be used in clinical laboratories to diagnose disease disorders.

Translation and evaluation of a pre-clinical 5-protein response prediction signature in a breast cancer phase Ib clinical trial

Human protein biomarker discovery relies heavily on pre-clinical models, in particular established cell lines and patient-derived xenografts, but confirmation studies in primary tissue are essential to demonstrate clinical relevance. We describe in this study the process that was followed to clinically translate a 5-protein response signature predictive for the activity of an anti-HER3 monoclonal antibody (lumretuzumab) originally measured in fresh frozen xenograft tissue. We detail the development, qualification, and validation of the multiplexed targeted mass spectrometry assay used to assess the signature performance in formalin-fixed, paraffin-embedded human clinical samples collected in a phase Ib trial designed to evaluate lumretuzumab in patients with metastatic breast cancer. We believe that the strategy delineated here provides a path forward to avoid the time- and cost-consuming step of having to develop immunological reagents against unproven targets. We expect that mass spectrometry-based platforms may become part of a rational process to rapidly test and qualify large number of candidate biomarkers to identify the few that stand a chance for further development and validation.

Liquid biopsy for pediatric central nervous system tumors

Central nervous system (CNS) tumors are the most common solid tumors in children, and the leading cause of cancer-related death. Over the past decade, molecular profiling has been incorporated into treatment for pediatric CNS tumors, allowing for a more personalized approach to therapy. Through the identification of tumor-specific changes, it is now possible to diagnose, assign a prognostic subgroup, and develop targeted chemotherapeutic treatment plans for many cancer types. The successful incorporation of informative liquid biopsies, where the liquid biome is interrogated for tumor-associated molecular clues, has the potential to greatly complement the precision-based approach to treatment, and ultimately, to improve clinical outcomes for children with CNS tumors. In this article, the current application of liquid biopsy in cancer therapy will be reviewed, as will its potential for the diagnosis and therapeutic monitoring of pediatric CNS tumors.

Early detection of lung cancer biomarkers through biosensor technology: A review

Lung cancer is undoubtedly one of the most serious health issues of the 21 st century. It is the second leading cause of cancer-related deaths in both men and women　worldwide, accounting for about 1.5 million deaths annually. Despite advances in the treatment of lung cancer with new pharmaceutical products and technological improvements, morbidity and mortality rates remains a significant challenge for the cancer biologists and oncologists. The vast majority of lung cancer patients present with advanced-stage of pathological process that ultimately leads to poor prognosis and a five-year survival rate less than 20%. Early and accurate screening and analysis using cost-effective means are urgently needed to effectively diagnose the disease, improve the survival rate or to reduce mortality and morbidity associated with lung cancer patients. Thus, the only hope for early recognition of risk factors and timely diagnosis and treatment of lung cancer is biosensors technology. Novel biosensing based diagnostics approaches for predicting metastatic risks are likely to have significant therapeutic and clinical impact in the near future. This article systematically provides a brief overview of various biosensing platforms for identification of lung cancer disease biomarkers, with a specific focus on recent advancements in electrochemical and optical biosensors, analytical performances of different biosensors, challenges and further research opportunities for routine clinical analysis.

Comparative proteome analysis of the capsule from patients with frozen shoulder

BACKGROUND

The etiology of frozen shoulder (FS) is unclear. Accordingly, this study used a label-free quantitative shotgun proteomic approach to elucidate the pathogenesis of FS based on protein expression levels.

METHODS

Tissue samples from the rotator interval (RI), middle glenohumeral ligament (MGHL), and anterior-inferior glenohumeral ligament (IGHL) were collected from 12 FSs with severe stiffness and 7 shoulders with a rotator cuff tear (RCT) as controls. Protein mixtures were digested and analyzed by nano-liquid chromatography/electrospray ionization-tandem mass spectrometry. Relative protein expression levels were calculated by the signal intensity of identified peptide ions on mass spectra. Differentially expressed proteins between FS and RCT samples were evaluated by a gene enrichment analysis using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes.

RESULTS

We identified 1594 proteins, 1358 of which were expressed in all 6 tissue groups. We detected more upregulated proteins in the upper (RI and MGHL) FS groups and the lower (IGHL) RCT group than in the comparative groups, respectively. Various proteins with functions in tissue repair, collagen metabolism and fibrillation, cell-cell and cell-matrix adhesion, blood coagulation, and the immune response were expressed more highly in the RI and MGHL FS groups than in the RCT group. Proteins with functions in phagocytosis, glutathione metabolism, retinoid metabolism, and cholesterol metabolism were expressed more highly in the IGHL RCT group than in the FS group.

CONCLUSIONS

The pathophysiology of FS differs between the upper and lower parts of the joint capsule. Different treatment strategies for FS may be appropriate, depending on the location.

Utilizing Ion Mobility to Identify Isobaric Post-Translational Modifications: Resolving Acrolein and Propionyl Lysine Adducts by TIMS Mass Spectrometry

Protein post-translational modifications provide critical proteomic details towards elucidating mechanisms of altered protein function due to toxic exposure, altered metabolism, or disease pathogenesis. Lysine propionylation is a recently described modification that occurs due to metabolic alterations in propionyl-CoA metabolism and sirtuin depropionylase activity. Acrolein is a toxic aldehyde generated through exogenous and endogenous pathways, such as industrial exposure, cigarette smoke inhalation, and non-enzymatic lipid peroxidation. Importantly, lysine modifications arising from propionylation and acroleination can be isobaric - indistinguishable by mass spectrometry - and inseparable via reverse-phase chromatography. Here, we present the novel application of trapped ion mobility spectrometry (TIMS) to resolve such competing isobaric lysine modifications. Specifically, the PTM products of a small synthetic peptide were analyzed using a prototype TIMS - time-of-flight mass spectrometer (TIMS-TOF). In that the mobilities of these propionylated and acroleinated peptides differ by only 1%, a high-resolution mobility analysis is required to resolve the two. We were able to achieve more than sufficient resolution in the TIMS analyzer (~170), readily separating these isobars.

Detection and localization of surgically resectable cancers with a multi-analyte blood test

Earlier detection is key to reducing cancer deaths. Here, we describe a blood test that can detect eight common cancer types through assessment of the levels of circulating proteins and mutations in cell-free DNA. We applied this test, called CancerSEEK, to 1005 patients with nonmetastatic, clinically detected cancers of the ovary, liver, stomach, pancreas, esophagus, colorectum, lung, or breast. CancerSEEK tests were positive in a median of 70% of the eight cancer types. The sensitivities ranged from 69 to 98% for the detection of five cancer types (ovary, liver, stomach, pancreas, and esophagus) for which there are no screening tests available for average-risk individuals. The specificity of CancerSEEK was greater than 99%: only 7 of 812 healthy controls scored positive. In addition, CancerSEEK localized the cancer to a small number of anatomic sites in a median of 83% of the patients.

Proteomics for early detection of colorectal cancer: recent updates

INTRODUCTION

Colorectal cancer (CRC) is a common type of cancer with a relatively poor survival rate. The survival rate of patients could be improved if CRC is detected early. Biomarkers associated with early stages of tumor development might provide useful tools for the early diagnosis of colorectal cancer. Areas covered: Online searches using PubMed and Google Scholar were performed using keywords and with a focus on recent proteomic studies. The aim of this review is to highlight the need for biomarkers to improve the detection rate of early CRC and provide an overview of proteomic technologies used for biomarker discovery and validation. This review will also discuss recent proteomic studies which focus on identifying biomarkers associated with the early stages of CRC development. Expert commentary: A large number of CRC biomarkers are increasingly being identified by proteomics using diverse approaches. However, the clinical relevance and introduction of these markers into clinical practice cannot be determined without a robust validation process. The size of validation cohorts remains a major limitation in many biomarker studies.

Optomechanical devices for deep plasma cancer proteomics

Most of the cancer deaths could be avoided by early detection of the tumor when it is confined to its primary site and it has not metastasized. To this aim, one of the most promising strategies is the discovery and detection of protein biomarkers shed by the young tumor to the bloodstream. Proteomic technologies, mainly mass spectrometry and multiplexed immunoassays, have rapidly developed during last years with improved limits of detection and multiplexing capability. Unfortunately, these developments together major investments and large international efforts have not resulted into new useful protein biomarkers. Here, we analyze the potential and limitations of current proteomic technologies for detecting protein biomarkers released into circulation by the tumor. We find that these technologies can hardly probe the deepest region of the plasma proteome, at concentrations below the pg/mL level, where protein biomarkers for early cancer detection may exist. This clearly indicates the need of incorporating novel ultrasensitive techniques to the proteomic tool-box that can cover the inaccessible regions of the plasma proteome. We here propose biological detectors based on nanomechanical systems for discovery and detection of cancer protein biomarkers in plasma. We review the modes of operation of these devices, putting our focus on recent developments on nanomechanical sandwich immunoassays and nanomechanical spectrometry. The first technique enables reproducible immunodetection of proteins at concentrations well below the pg/mL level, with a limit of detection on the verge of 10 ag/mL. This technology can potentially detect low abundance tumor-associated proteins in plasma at the very early stages of the tumor. The second technique enables the identification of individual intact proteins by two physical coordinates, the mass and stiffness, instead of the mass-to-charge ratio of the protein constituents. This technology enormously simplifies the identification of proteins and it can provide useful information on interactions and posttranslational modifications, that otherwise is lost in mass spectrometry.

Quantification of mutant SPOP proteins in prostate cancer using mass spectrometry-based targeted proteomics

BACKGROUND

Speckle-type POZ protein (SPOP) is an E3 ubiquitin ligase adaptor protein that functions as a potential tumor suppressor, and SPOP mutations have been identified in ~10% of human prostate cancers. However, it remains unclear if mutant SPOP proteins can be utilized as biomarkers for early detection, diagnosis, prognosis or targeted therapy of prostate cancer. Moreover, the SPOP mutation sites are distributed in a relatively short region with multiple lysine residues, posing significant challenges for bottom-up proteomics analysis of the SPOP mutations.

METHODS

To address this issue, PRISM (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing)-SRM (selected reaction monitoring) mass spectrometry assays have been developed for quantifying wild-type SPOP protein and 11 prostate cancer-derived SPOP mutations.

RESULTS

Despite inherent limitations due to amino acid sequence constraints, all the PRISM-SRM assays developed using Arg-C digestion showed a linear dynamic range of at least two orders of magnitude, with limits of quantification ranged from 0.1 to 1 fmol/μg of total protein in the cell lysate. Applying these SRM assays to analyze HEK293T cells with and without expression of the three most frequent SPOP mutations in prostate cancer (Y87N, F102C or F133V) led to confident detection of all three SPOP mutations in corresponding positive cell lines but not in the negative cell lines. Expression of the F133V mutation and wild-type SPOP was at much lower levels compared to that of F102C and Y87N mutations; however, at present, it is unknown if this also affects the biological activity of the SPOP protein.

CONCLUSIONS

In summary, PRISM-SRM enables multiplexed, isoform-specific detection of mutant SPOP proteins in cell lysates, providing significant potential in biomarker development for prostate cancer.

An epitope imprinted polymer with affinity for kininogen fragments prepared by metal coordination interaction for cancer biomarker analysis

A MIP with affinity for kininogen fragments was synthesized by epitope imprinting for biomarker analysis in serum.